Ingroup biases and cognitive load (Experiment 2)
Notice bibliographique
Résumé
NOTE: this registration was initially drafted prior to data collection. We have now collected data from 72 participants (prior to exclusion) but have not looked at the data yet in order to maintain the integrity of this registration. Given the results from our initial experiment, we have decided to proceed with a follow-up experiment. In this experiment we will explore whether cognitive load differently impacts the way individuals allocate chips when the ingroup is based on an external grouper (university) vs. an internal grouper (shared social preference). In the first experiment, we had a target N of ~70 but were left with a number of exclusions that led to a smaller number of useable participants. Thus, in the follow-up experiment, we aimed for an N of about 80, while also aiming to conclude collecting data by the end of the academic term due to time constraints of this honours thesis. The “internal” grouper will be whether the hypothetical other players share or don’t share the participant’s preference for intimate gatherings vs. large parties, which participants will indicate prior to completing the experimental task. The task they will complete will be almost identical to the initial casino chip allocation task in Experiment 1, but the names of the fellow players will be removed and that hypothetical player’s social preference (i.e., intimate gatherings vs large parties) will be added. This change entails that on any given trial, participants will see a “fellow player” who is considered to be a double-ingroup member (UNSW and social preference), partial-ingroup (UNSW and other preference), partial-outgroup (McGill and shared preference) or double outgroup member (McGill and other preference). Based on the results from Experiment 1, we anticipate that fewer chips will be allocated to McGill students than to UNSW students, but that this will primarily occur under cognitive load. This would replicate the results of Experiment 1, with the impact of an externally-based grouper emerging most robustly under load. The critical question for Experiment 2 is what happens when ingroup/outgroup is defined via something more internally based such as shared social preference. We predict that – in contrast to the impact of an external grouper – the impact of an internally-based grouper will be most robust under no load (we reason that it would take more cognitive resources to activate internally driven, more abstract group membership criteria). This may be reflected in a weaker main effect of internal-grouper under load, and it may also be reflected in a weaker degree (under load) to which the internal grouper modulates the impact of the external grouper (whereas the degree to which the external grouper modulates the impact of the internal grouper will be weaker under no load). Additionally, we will assess whether chip allocation is greatest for double-ingroup players (i.e., same university and movie preference as the participant), least for double-outgroup players, and at an intermediate level for half-ingroup/half-outgroup players. Consistent with Experiment 1, we intend to exclude participants who have less than a 75% accuracy rate on their cognitive load trials, i.e. if they get less than 15 out of 20 correct. This is because we cannot be sure that they engaged with the cognitive load task to the best of their ability.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».